Computational and Mathematical Methods in Medicine
Volume 11 (2010), Issue 2, Pages 185-199
doi:10.1080/17486700903170712
Original Article

Towards Prediction of HCV Therapy Efficiency

Szymon Wasik,1 Paulina Jackowiak,2 Jacek B. Krawczyk,3 Paweł Kedziora,1 Piotr Formanowicz,1,2 Marek Figlerowicz,2 and Jacek Błażewicz1,2

1Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965 Poznan, Poland
2Institute of Bioorganic Chemistry, Polish Academy of Sciences, Z. Noskowskiego 12/14, 61-704 Poznan, Poland
3Faculty of Commerce and Administration, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

Received 6 February 2009; Accepted 30 June 2009

Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We investigate a correlation between genetic diversity of hepatitis C virus population and the level of viral RNA accumulation in patient blood. Genetic diversity is defined as the mean Hamming distance between all pairs of virus RNA sequences representing the population. We have found that a low Hamming distance (i.e. low genetic diversity) correlates with a high RNA level; symmetrically, high diversity corresponds to a low RNA level. We contend that the obtained correlation strength justifies the use of the viral RNA level as a measure enabling prediction of efficiency of an established therapy. We also propose that patient qualification for therapy, based on viral RNA level, improves its efficiency.